For companies awash in data, it can be difficult to mine the historical patterns that could help them make better business decisions. While they have the data, companies may lack the time to unlock it and make changes in behavior.

Consider Johnson & Johnson (J&J), a consumer health company, based in New Brunswick, N.J., that has decades of testing data on its products, such as skin irritation tests. With the right tools and human insights, researchers could unearth discoveries about testing that could save time and money. But the company has to be able to see and make use of those data patterns first.

Enter Wellspring's Sophiaknowledge management system. According to Russel Walters, a research fellow at J&J, 30 years of product testing data can reveal findings that save money and time. The company conducts irritation tests on every product to be launched. But decades of data also revealed that subjects were more likely to have reactions during the month of April, when allergy season begins. Walters noted that over the years, the team has likely dumped formulas that might have passed muster at other times of year, but failed the test and prompted false positive reactions because of the start of allergy season.

"We should have been controlling for that, but we didn't know," Walters said. "So, we were probably throwing out some formulas that were fine, but causing reactions because of the time of year."

In another case, Johnson & Johnson had developed a tried-and-true No More Tears test for baby shampoo. But another test, which was better but not used to screen its products, revealed itself to be even more accurate, and J&J was able to identify that through the data that resides in Sophia.

"We were able to justify it as a superior test and switch from the older to a newer test, and the validation, which usually costs $1 million -- we had that data already," Walters said. "We have a more accurate test and don't screen out as many false positives."

Mapping social networks

Walters said that Sophia also offers the opportunity for J&J to think about social networks as a chance to engineer for operational efficiency. Researchers like Walters have hundreds of connections to other collaborators, through their work on contracts and patents, as well as their collaborations on research papers. But none of these collaborations have been formally documented.

Walters noted that, according to Dunbar's Number, we can maintain only 150 fruitful social connections at one time, so researchers need to be judicious about how they cultivate their professional network and, possibly, focus less on colleagues internal to an organization and more on collaboration with external sources.

The data in Sophia's knowledge management system -- the number of papers collaborated on with certain researchers, the patents with various researchers and so on -- could provide a window to those connections.

So, Walters noted, J&J hopes to start an initiative that can help researchers map out their most fruitful and frequent collaborations in Sophia, using the data on their collaborations, such as coauthored papers.

"A network exists and can be visualized," Walters said. "Organizations should be optimizing what that relationship network looks like."

But Walters acknowledged that the human element may get in the way of making use of the data in Sophia. Having researchers be more transparent and think about changing behavior to optimize connections may take work. They need to see the value of social networking to their central roles as product developers.